Main techniques
Averaging
- Visualise group differences
Transforming
- Sexual dimorphism
Abstract
Face stimuli are commonly created in non-reproducible ways. This paper will introduce the open-access online platform webmorph and its associated R package webmorphR. It will explain the technical processes of morphing and transforming through a case study of creating face stimuli from an open-access image set.
People manipulate faces.
Give some examples.
Scope of this type of research.
I gave up on a research project once because I couldn’t figure out how to manipulate spatial scale in MatLab to make my stimuli look like a relevant paper. When I contacted the author, they didn’t know how the stimuli were created because a postdoc just did it in photoshop.
Faces are sampled, so replications should sample new faces as well as new participants.
Difficulty in creating equivalent face stimuli is a barrier to this, resulting in stimulus sets that are used across dozens or hundreds of papers.
Automatic versus manual delineation.
Why normalise?
2 point versus Procrustes normalisation (in webmorphR)
lisa <- faces("lisa")
orig <- plot(lisa, pt.plot = TRUE, labels = "", nrow = 1)
twopt <- align(lisa, pt1 = 63, pt2 = 81, patch = TRUE) %>%
plot(pt.plot = TRUE, labels = "", nrow = 1)
# any two points that are standard on the image
# should work for procrustes alignment
lisa_proc <- align(lisa, pt1 = 63, pt2 = 81,
procrustes = TRUE, patch = TRUE)
procr <- plot(lisa_proc, pt.plot = TRUE,
labels = "", nrow = 1)
cowplot::plot_grid(orig, twopt, procr, nrow = 3,
labels = c("original", "two-point", "procrustes"))
(effect in masc paper)
Texture/no
How this is different from LL/RR mirroring.
Continuum
We used R [Version 4.0.2; R Core Team (2020)] and the R-packages papaja [Version 0.1.0.9997; Aust and Barth (2020)], and webmorph [Version 0.0.0.9001; DeBruine (2020)] to produce this manuscript.